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How much do Investors Rely on Credit Ratings: Empirical evidence from the U.S. and E.U. CLO primary market

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Abstract

We investigate the extent to which investors rely on credit ratings and other factors beyond credit ratings in determining the funding cost for collateralized loan obligations (CLOs) tranches in the period 1997-2015. We find significant differences between the United States (U.S.) and European Union (E.U.) markets. In the U.S., we find a much higher and more consistent degree of reliance on credit ratings and other factors in pricing CLOs over time compared to the E.U. market. Finally, we find that investors in both markets reduce, rather than increase, funding costs when rating standards loosened. The implications for market practices are discussed.

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Notes

  1. In 2018, CLO issuance in the U.S. market amounted to roughly $125 billion (S&P Global Market Intelligence 2018) and at the same time period in the E.U. market it was approximately €28 billion (Bloomberg 2019).

  2. Dodd-Frank Act 2010, Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010, Section 941 Subsection 15G.

  3. Regulation (EU) No 462/2013 of the European Parliament and of the Council of 21 May 2013 amending Regulation (EC) No 1060/2009 on CRAs.

  4. There are studies that focus on other factors besides credit rating that determine the pricing of structured products. For example, Deku et al. (2019), using a sample of 4,201 European originated MBS tranches show that the quality of the trustee has an impact on the pricing of structured finance securities during the most recent global financial crisis.

  5. If fixed-rate tranches were to be included in our study, then it would be necessary to determine the appropriate benchmark yield curve for each tranche in the sample in order to obtain primary issuance spreads that could be consistently compared across the sample. By restricting the tranches in our sample to 3-month floating-rate tranches where the reference rate is the same interest rate benchmark, we avoid this problem. Furthermore, in constructing the final sample, we had to eliminate some tranches due to errant data or metrics that represented vastly atypical observations. For our analysis, we want to have a consistent benchmark for assessing the funding cost.

  6. EURIBOR reflects the interest rate at which highly credit rated banks can borrow, in euros, from other banks on an unsecured basis. USD LIBOR reflects the interest rate at which highly credit rated banks can borrow, in U.S. dollars, from other banks on an unsecured basis. EURIBOR and USD LIBOR are determined and communicated on a daily basis for a variety of maturities.

  7. We excluded one outlier with 29 tranches in one deal.

  8. Our sample only includes EU tranches that are issued from 1999 – 2015. To make an accurate comparison on changes in rating standards over time, we exclude all US tranches that are issued before 1999 in this regression.

  9. We use the triple A sample to test if the effects are consistent if we use a fixed rating category, in line with Alp (2013). There are an insufficient number of observations for the other fixed rating categories to enable statistical analyses.

  10. The rating notch length in our sample is (3.75 – (-8.20))/19=0.63. The coefficient of rating discrepancy in Panel A of Table 5 is 0.57 with a standard deviation of 0.50 (see Table 1, Panel A). Hence, a one-standard-deviation increase in rating discrepancy increases the credit rating by 0.57*0.50/0.63= 0.453 notches.

  11. In unreported tests we have also included the security design factors in our model and obtained similar results.

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Authors and Affiliations

  1. EDHEC Risk Institute, France, 393-400 Promenade des Anglais, BP 3116, 06202, Nice Cedex 3, France

    Frank Fabozzi

  2. Nyenrode Business Universiteit, The Netherlands, Straatweg 25 3621 BG, Breukelen, The Netherlands

    Vivian M. van Breemen, Dennis Vink & Mike Nawas

  3. Gamut Capital Management, United States, 250 West 55th Street, 36th Floor, New York, NY, 10019, USA

    Austin Gengos

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  1. Frank Fabozzi
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  2. Vivian M. van Breemen
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  3. Dennis Vink
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  4. Mike Nawas
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Correspondence to Vivian M. van Breemen.

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Fabozzi, F., van Breemen, V.M., Vink, D. et al. How much do Investors Rely on Credit Ratings: Empirical evidence from the U.S. and E.U. CLO primary market. J Financ Serv Res 63, 221–247 (2023). https://doi.org/10.1007/s10693-021-00372-x

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  • DOI: https://doi.org/10.1007/s10693-021-00372-x

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